Overview

Dataset statistics

Number of variables26
Number of observations6378
Missing cells4766
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory865.6 B

Variable types

NUM15
CAT10
URL1

Warnings

county has constant value "6378" Constant
address has a high cardinality: 6333 distinct values High cardinality
subdivision has a high cardinality: 4593 distinct values High cardinality
lot has a high cardinality: 483 distinct values High cardinality
zoning has a high cardinality: 1068 distinct values High cardinality
date has a high cardinality: 575 distinct values High cardinality
longitude is highly correlated with latitudeHigh correlation
latitude is highly correlated with longitudeHigh correlation
bathrooms is highly correlated with home_size and 1 other fieldsHigh correlation
home_size is highly correlated with bathroomsHigh correlation
bedrooms is highly correlated with bathroomsHigh correlation
latitude has 75 (1.2%) missing values Missing
longitude has 75 (1.2%) missing values Missing
home_size has 310 (4.9%) missing values Missing
lot_size has 94 (1.5%) missing values Missing
year_built has 273 (4.3%) missing values Missing
subdivision has 493 (7.7%) missing values Missing
census has 71 (1.1%) missing values Missing
tract has 71 (1.1%) missing values Missing
lot has 408 (6.4%) missing values Missing
sale_price has 220 (3.4%) missing values Missing
estimated_value has 562 (8.8%) missing values Missing
crime_index has 900 (14.1%) missing values Missing
school quality has 92 (1.4%) missing values Missing
bedrooms has 543 (8.5%) missing values Missing
bathrooms has 543 (8.5%) missing values Missing
home_size is highly skewed (γ1 = 26.96742565) Skewed
lot_size is highly skewed (γ1 = 31.11441822) Skewed
address is uniformly distributed Uniform
subdivision is uniformly distributed Uniform
sex_offenders has 1078 (16.9%) zeros Zeros

Reproduction

Analysis started2020-10-19 22:38:47.398248
Analysis finished2020-10-19 22:39:54.250884
Duration1 minute and 6.85 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct6083
Distinct (%)96.5%
Missing75
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean-52.14586352
Minimum-118.882753
Maximum34.818751
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:54.420105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-118.882753
5-th percentile-118.5514559
Q1-118.3173215
median-118.015177
Q334.0611945
95-th percentile34.4215194
Maximum34.818751
Range153.701504
Interquartile range (IQR)152.378516

Descriptive statistics

Standard deviation75.52943973
Coefficient of variation (CV)-1.448426292
Kurtosis-1.929439689
Mean-52.14586352
Median Absolute Deviation (MAD)0.507019
Skewness0.2667189681
Sum-328675.3778
Variance5704.696265
MonotocityNot monotonic
2020-10-19T15:39:54.643164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-118.2375310.5%
 
-118.0618200.3%
 
34.6881170.3%
 
34.6867110.2%
 
34.147933100.2%
 
34.529380.1%
 
34.597660.1%
 
-117.963250.1%
 
-118.197450.1%
 
34.486340.1%
 
34.275440.1%
 
-118.095940.1%
 
-118.51729540.1%
 
-118.3299483< 0.1%
 
-118.5763553< 0.1%
 
-118.079183< 0.1%
 
-118.4862053< 0.1%
 
-118.3065093< 0.1%
 
33.789023< 0.1%
 
34.10132< 0.1%
 
33.7716082< 0.1%
 
33.7657882< 0.1%
 
34.6615432< 0.1%
 
-118.28692< 0.1%
 
-118.1725712< 0.1%
 
Other values (6058)614496.3%
 
(Missing)751.2%
 
ValueCountFrequency (%) 
-118.8827531< 0.1%
 
-118.8507791< 0.1%
 
-118.8505571< 0.1%
 
-118.824651< 0.1%
 
-118.8196531< 0.1%
 
-118.8102591< 0.1%
 
-118.8083051< 0.1%
 
-118.7976251< 0.1%
 
-118.7947031< 0.1%
 
-118.788541< 0.1%
 
ValueCountFrequency (%) 
34.8187511< 0.1%
 
34.7940871< 0.1%
 
34.7787251< 0.1%
 
34.7395712< 0.1%
 
34.7178491< 0.1%
 
34.7167991< 0.1%
 
34.7139531< 0.1%
 
34.7134281< 0.1%
 
34.7131461< 0.1%
 
34.7127921< 0.1%
 

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct6080
Distinct (%)96.5%
Missing75
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean-32.0132825
Minimum-118.860729
Maximum34.782459
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:54.882010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-118.860729
5-th percentile-118.5264936
Q1-118.242056
median33.855251
Q334.1069495
95-th percentile34.4872279
Maximum34.782459
Range153.643188
Interquartile range (IQR)152.3490055

Descriptive statistics

Standard deviation75.52841886
Coefficient of variation (CV)-2.359283802
Kurtosis-1.929439596
Mean-32.0132825
Median Absolute Deviation (MAD)0.568483
Skewness-0.2667163621
Sum-201779.7196
Variance5704.542055
MonotocityNot monotonic
2020-10-19T15:39:55.670682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34.6867310.5%
 
34.6881200.3%
 
-118.0618170.3%
 
-118.2375110.2%
 
-118.414091100.2%
 
-117.963280.1%
 
-117.839860.1%
 
34.486350.1%
 
34.529350.1%
 
34.16981940.1%
 
34.56940.1%
 
-118.197440.1%
 
-118.550540.1%
 
-118.0594373< 0.1%
 
34.1045013< 0.1%
 
34.4107023< 0.1%
 
-118.1351213< 0.1%
 
33.9912113< 0.1%
 
34.1691323< 0.1%
 
33.9597562< 0.1%
 
34.162112< 0.1%
 
-118.3395172< 0.1%
 
33.8656882< 0.1%
 
34.25472< 0.1%
 
-118.4241032< 0.1%
 
Other values (6055)614496.3%
 
(Missing)751.2%
 
ValueCountFrequency (%) 
-118.8607291< 0.1%
 
-118.8529171< 0.1%
 
-118.8525381< 0.1%
 
-118.8335311< 0.1%
 
-118.8328011< 0.1%
 
-118.8256621< 0.1%
 
-118.8162311< 0.1%
 
-118.8137931< 0.1%
 
-118.7943261< 0.1%
 
-118.7939561< 0.1%
 
ValueCountFrequency (%) 
34.7824591< 0.1%
 
34.7139111< 0.1%
 
34.7124561< 0.1%
 
34.7103931< 0.1%
 
34.7097891< 0.1%
 
34.7096161< 0.1%
 
34.7095131< 0.1%
 
34.70931< 0.1%
 
34.7059291< 0.1%
 
34.7059091< 0.1%
 

address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6333
Distinct (%)99.5%
Missing11
Missing (%)0.2%
Memory size50.0 KiB
Vac/donatello St/vic Lamour Ct
 
4
Vac/pillsbury St/vic Trevor Ave
 
4
Vac/oldfield St/vic 63rd Stw
 
3
5455 11th Ave
 
3
Vac/soledad Canyon Rd/vic Crow Vly
 
3
Other values (6328)
6350 
ValueCountFrequency (%) 
Vac/donatello St/vic Lamour Ct40.1%
 
Vac/pillsbury St/vic Trevor Ave40.1%
 
Vac/oldfield St/vic 63rd Stw3< 0.1%
 
5455 11th Ave3< 0.1%
 
Vac/soledad Canyon Rd/vic Crow Vly3< 0.1%
 
Vac/ave J7/vic 51st Stw3< 0.1%
 
21821 Hawaiian Ave2< 0.1%
 
7131 Valjean Ave2< 0.1%
 
330 W Pillsbury St2< 0.1%
 
4033 Chevy Chase Dr2< 0.1%
 
7602 Irondale Ave2< 0.1%
 
Vac/ave Y8/vic Lawson Ct2< 0.1%
 
19845 Collins Rd # 3472< 0.1%
 
Vac/ave R6/vic Longview Rd2< 0.1%
 
315 Parsons Lndg2< 0.1%
 
2207 21st St2< 0.1%
 
631 N Lincoln St2< 0.1%
 
1428 S Marengo Ave2< 0.1%
 
2235 Sunset Crest Dr2< 0.1%
 
735 Verde Vista Ave2< 0.1%
 
Vac/63rd Stw/vic Ovington St2< 0.1%
 
Vac/170th/171st Ste/vic Park Vly2< 0.1%
 
6300 Chalet Dr2< 0.1%
 
14150 S Figueroa St2< 0.1%
 
4025 Berenice Pl2< 0.1%
 
Other values (6308)630998.9%
 
(Missing)110.2%
 
2020-10-19T15:39:56.061516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6307 ?
Unique (%)99.1%
2020-10-19T15:39:56.505237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length17
Mean length17.53637504
Min length3

Overview of Unicode Properties

Unique unicode characters66
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1757115.7%
 
e68206.1%
 
155535.0%
 
a50084.5%
 
241753.7%
 
t41663.7%
 
r40573.6%
 
n38503.4%
 
l34673.1%
 
034073.0%
 
o32932.9%
 
332032.9%
 
v31482.8%
 
429412.6%
 
i27492.5%
 
527442.5%
 
A26602.4%
 
S26362.4%
 
d21361.9%
 
621191.9%
 
718621.7%
 
817611.6%
 
s16131.4%
 
915561.4%
 
h12091.1%
 
Other values (41)1814316.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4849943.4%
 
Decimal Number2932126.2%
 
Space Separator1757115.7%
 
Uppercase Letter1510913.5%
 
Other Punctuation13431.2%
 
Dash Punctuation4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1555318.9%
 
2417514.2%
 
0340711.6%
 
3320310.9%
 
4294110.0%
 
527449.4%
 
621197.2%
 
718626.4%
 
817616.0%
 
915565.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
17571100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A266017.6%
 
S263617.4%
 
C10797.1%
 
D10767.1%
 
W8975.9%
 
B8525.6%
 
L7074.7%
 
P6724.4%
 
E6394.2%
 
R6394.2%
 
M5213.4%
 
V4933.3%
 
N4252.8%
 
H3622.4%
 
G3242.1%
 
O2431.6%
 
F2381.6%
 
T2311.5%
 
K1551.0%
 
J1230.8%
 
I730.5%
 
Q240.2%
 
Y200.1%
 
Z90.1%
 
U7< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e682014.1%
 
a500810.3%
 
t41668.6%
 
r40578.4%
 
n38507.9%
 
l34677.1%
 
o32936.8%
 
v31486.5%
 
i27495.7%
 
d21364.4%
 
s16133.3%
 
h12092.5%
 
c11212.3%
 
u10122.1%
 
y9492.0%
 
m8111.7%
 
w6211.3%
 
g6131.3%
 
k5221.1%
 
b4200.9%
 
p3700.8%
 
f3350.7%
 
z970.2%
 
x690.1%
 
j270.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#107279.8%
 
/27120.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6360856.9%
 
Common4823943.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
1757136.4%
 
1555311.5%
 
241758.7%
 
034077.1%
 
332036.6%
 
429416.1%
 
527445.7%
 
621194.4%
 
718623.9%
 
817613.7%
 
915563.2%
 
#10722.2%
 
/2710.6%
 
-4< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e682010.7%
 
a50087.9%
 
t41666.5%
 
r40576.4%
 
n38506.1%
 
l34675.5%
 
o32935.2%
 
v31484.9%
 
i27494.3%
 
A26604.2%
 
S26364.1%
 
d21363.4%
 
s16132.5%
 
h12091.9%
 
c11211.8%
 
C10791.7%
 
D10761.7%
 
u10121.6%
 
y9491.5%
 
W8971.4%
 
B8521.3%
 
m8111.3%
 
L7071.1%
 
P6721.1%
 
E6391.0%
 
Other values (27)698111.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII111847100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1757115.7%
 
e68206.1%
 
155535.0%
 
a50084.5%
 
241753.7%
 
t41663.7%
 
r40573.6%
 
n38503.4%
 
l34673.1%
 
034073.0%
 
o32932.9%
 
332032.9%
 
v31482.8%
 
429412.6%
 
i27492.5%
 
527442.5%
 
A26602.4%
 
S26362.4%
 
d21361.9%
 
621191.9%
 
718621.7%
 
817611.6%
 
s16131.4%
 
915561.4%
 
h12091.1%
 
Other values (41)1814316.2%
 

property_type
Categorical

Distinct44
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
Single Family Residence
3921 
Condominium
1276 
Duplex (2 units, any combination)
 
203
Planned Unit Development (PUD)
 
181
Residential - Vacant Land
 
160
Other values (39)
637 
ValueCountFrequency (%) 
Single Family Residence392161.5%
 
Condominium127620.0%
 
Duplex (2 units, any combination)2033.2%
 
Planned Unit Development (PUD)1812.8%
 
Residential - Vacant Land1602.5%
 
Apartment house (5+ units)1131.8%
 
Triplex (3 units, any combination)781.2%
 
MISCELLANEOUS (Vacant Land) 590.9%
 
Quadplex (4 Units, Any Combination)580.9%
 
Store, Retail Outlet 440.7%
 
Light Industrial (10% Improved Office space; Machine Shop)400.6%
 
Office Building340.5%
 
Warehouse, Storage300.5%
 
MISCELLANEOUS (Commercial)290.5%
 
Gas Station220.3%
 
Parking Garage, Parking Structure210.3%
 
Vacant Commercial190.3%
 
Industrial - Vacant Land150.2%
 
Parcel Number100.2%
 
Restaurant90.1%
 
Mobile home80.1%
 
Store/Office (mixed use)80.1%
 
Community: Shopping Center, Mini-Mall70.1%
 
Hotel/Motel40.1%
 
Public School (administration, campus, dorms, instruction)40.1%
 
Other values (19)250.4%
 
2020-10-19T15:39:56.784878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique14 ?
Unique (%)0.2%
2020-10-19T15:39:57.175282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length58
Median length23
Mean length21.69504547
Min length7

Overview of Unicode Properties

Unique unicode characters61
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1796113.0%
 
i1657712.0%
 
n136219.8%
 
119308.6%
 
l90136.5%
 
m73045.3%
 
a63004.6%
 
d59884.3%
 
s48503.5%
 
c47313.4%
 
S42783.1%
 
y42743.1%
 
R41393.0%
 
g41093.0%
 
F39262.8%
 
o38752.8%
 
u23151.7%
 
t22741.6%
 
C14891.1%
 
(7820.6%
 
)7820.6%
 
p7780.6%
 
r6440.5%
 
D5650.4%
 
U5110.4%
 
Other values (36)53553.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10605776.6%
 
Uppercase Letter1741612.6%
 
Space Separator119308.6%
 
Open Punctuation7820.6%
 
Close Punctuation7820.6%
 
Other Punctuation5750.4%
 
Decimal Number5320.4%
 
Dash Punctuation1840.1%
 
Math Symbol1130.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S427824.6%
 
R413923.8%
 
F392622.5%
 
C14898.5%
 
D5653.2%
 
U5112.9%
 
L4522.6%
 
P4322.5%
 
A2601.5%
 
V2561.5%
 
O2151.2%
 
I1871.1%
 
E1801.0%
 
M1600.9%
 
N1000.6%
 
T810.5%
 
Q580.3%
 
G470.3%
 
B380.2%
 
W320.2%
 
H100.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1796116.9%
 
i1657715.6%
 
n1362112.8%
 
l90138.5%
 
m73046.9%
 
a63005.9%
 
d59885.6%
 
s48504.6%
 
c47314.5%
 
y42744.0%
 
g41093.9%
 
o38753.7%
 
u23152.2%
 
t22742.1%
 
p7780.7%
 
r6440.6%
 
b3650.3%
 
x3470.3%
 
h2890.3%
 
v2280.2%
 
f1650.2%
 
k48< 0.1%
 
w1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11930100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(782100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
220338.2%
 
511321.2%
 
37814.7%
 
45810.9%
 
1407.5%
 
0407.5%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+113100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)782100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,46981.6%
 
%407.0%
 
;407.0%
 
/183.1%
 
:71.2%
 
.10.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-184100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12347389.2%
 
Common1489810.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1796114.5%
 
i1657713.4%
 
n1362111.0%
 
l90137.3%
 
m73045.9%
 
a63005.1%
 
d59884.8%
 
s48503.9%
 
c47313.8%
 
S42783.5%
 
y42743.5%
 
R41393.4%
 
g41093.3%
 
F39263.2%
 
o38753.1%
 
u23151.9%
 
t22741.8%
 
C14891.2%
 
p7780.6%
 
r6440.5%
 
D5650.5%
 
U5110.4%
 
L4520.4%
 
P4320.3%
 
b3650.3%
 
Other values (19)27022.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
1193080.1%
 
(7825.2%
 
)7825.2%
 
,4693.1%
 
22031.4%
 
-1841.2%
 
51130.8%
 
+1130.8%
 
3780.5%
 
4580.4%
 
1400.3%
 
0400.3%
 
%400.3%
 
;400.3%
 
/180.1%
 
:7< 0.1%
 
.1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII138371100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1796113.0%
 
i1657712.0%
 
n136219.8%
 
119308.6%
 
l90136.5%
 
m73045.3%
 
a63004.6%
 
d59884.3%
 
s48503.5%
 
c47313.4%
 
S42783.1%
 
y42743.1%
 
R41393.0%
 
g41093.0%
 
F39262.8%
 
o38752.8%
 
u23151.7%
 
t22741.6%
 
C14891.1%
 
(7820.6%
 
)7820.6%
 
p7780.6%
 
r6440.5%
 
D5650.4%
 
U5110.4%
 
Other values (36)53553.9%
 

home_size
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct2628
Distinct (%)43.3%
Missing310
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean2411.112393
Minimum3
Maximum295336.8
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:57.677245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile818.35
Q11230
median1625
Q32302
95-th percentile4953.65
Maximum295336.8
Range295333.8
Interquartile range (IQR)1072

Descriptive statistics

Standard deviation6256.050004
Coefficient of variation (CV)2.594673738
Kurtosis1009.672565
Mean2411.112393
Median Absolute Deviation (MAD)473
Skewness26.96742565
Sum14630630
Variance39138161.66
MonotocityNot monotonic
2020-10-19T15:39:58.096923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1570160.3%
 
1440160.3%
 
1260130.2%
 
1080130.2%
 
1540130.2%
 
1008130.2%
 
960130.2%
 
1520120.2%
 
1344120.2%
 
1580120.2%
 
1404120.2%
 
1400120.2%
 
1380120.2%
 
1760110.2%
 
1240110.2%
 
1320110.2%
 
1200110.2%
 
1116100.2%
 
1120100.2%
 
912100.2%
 
1288100.2%
 
1488100.2%
 
1420100.2%
 
1346100.2%
 
1176100.2%
 
Other values (2603)577590.5%
 
(Missing)3104.9%
 
ValueCountFrequency (%) 
32< 0.1%
 
1202< 0.1%
 
2101< 0.1%
 
2402< 0.1%
 
2901< 0.1%
 
3201< 0.1%
 
3602< 0.1%
 
3691< 0.1%
 
3711< 0.1%
 
3761< 0.1%
 
ValueCountFrequency (%) 
295336.81< 0.1%
 
188179.21< 0.1%
 
136778.41< 0.1%
 
111949.21< 0.1%
 
110206.81< 0.1%
 
102801.61< 0.1%
 
82328.41< 0.1%
 
71438.41< 0.1%
 
69260.41< 0.1%
 
68389.21< 0.1%
 

lot_size
Real number (ℝ≥0)

MISSING
SKEWED

Distinct4136
Distinct (%)65.8%
Missing94
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean42424.1344
Minimum283
Maximum12486474
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:58.494433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum283
5-th percentile3744.3
Q16006.75
median7653.5
Q317284.5
95-th percentile165963.6
Maximum12486474
Range12486191
Interquartile range (IQR)11277.75

Descriptive statistics

Standard deviation236622.9613
Coefficient of variation (CV)5.577555431
Kurtosis1385.745445
Mean42424.1344
Median Absolute Deviation (MAD)2467.5
Skewness31.11441822
Sum266593260.6
Variance5.599042579e+10
MonotocityNot monotonic
2020-10-19T15:39:58.796632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6000210.3%
 
5000210.3%
 
7500210.3%
 
23522.4180.3%
 
23086.8180.3%
 
6750160.3%
 
22651.2160.3%
 
24829.2150.2%
 
7499140.2%
 
26136140.2%
 
22215.6140.2%
 
23958140.2%
 
30056.4130.2%
 
6002130.2%
 
39639.6120.2%
 
29620.8110.2%
 
30492110.2%
 
5200110.2%
 
44431.2110.2%
 
13894100.2%
 
6751100.2%
 
4800100.2%
 
108900100.2%
 
36154.8100.2%
 
40946.4100.2%
 
Other values (4111)594093.1%
 
(Missing)941.5%
 
ValueCountFrequency (%) 
2831< 0.1%
 
7451< 0.1%
 
8051< 0.1%
 
8501< 0.1%
 
8521< 0.1%
 
8621< 0.1%
 
9051< 0.1%
 
9561< 0.1%
 
9571< 0.1%
 
9671< 0.1%
 
ValueCountFrequency (%) 
124864741< 0.1%
 
6970906.81< 0.1%
 
43560001< 0.1%
 
3588908.43< 0.1%
 
3168554.41< 0.1%
 
2319134.41< 0.1%
 
1904007.62< 0.1%
 
17946721< 0.1%
 
1775505.61< 0.1%
 
1719313.21< 0.1%
 

year_built
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)2.1%
Missing273
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1963.97674
Minimum1883
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:59.038918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1883
5-th percentile1923
Q11948
median1961
Q31984
95-th percentile2008
Maximum2020
Range137
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.25626274
Coefficient of variation (CV)0.01336892754
Kurtosis-0.5657084545
Mean1963.97674
Median Absolute Deviation (MAD)18
Skewness0.04549510281
Sum11990078
Variance689.3913331
MonotocityNot monotonic
2020-10-19T15:39:59.263103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19501692.6%
 
19551492.3%
 
19481442.3%
 
19541412.2%
 
19531382.2%
 
19521372.1%
 
19561322.1%
 
19511141.8%
 
19471101.7%
 
19791091.7%
 
19491051.6%
 
19891031.6%
 
19871001.6%
 
1923971.5%
 
1964961.5%
 
1963951.5%
 
1973951.5%
 
1958891.4%
 
1957891.4%
 
1959881.4%
 
1962851.3%
 
1941841.3%
 
1924811.3%
 
1981791.2%
 
1984791.2%
 
Other values (104)339753.3%
 
(Missing)2734.3%
 
ValueCountFrequency (%) 
18831< 0.1%
 
18851< 0.1%
 
18901< 0.1%
 
18921< 0.1%
 
18953< 0.1%
 
18971< 0.1%
 
18981< 0.1%
 
18991< 0.1%
 
19003< 0.1%
 
190140.1%
 
ValueCountFrequency (%) 
2020100.2%
 
2019350.5%
 
2018250.4%
 
2017230.4%
 
2016300.5%
 
2015360.6%
 
2014200.3%
 
2013180.3%
 
2012110.2%
 
2011130.2%
 

parcel_number
Real number (ℝ≥0)

Distinct6368
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5032195798
Minimum2004009012
Maximum8765016012
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:39:59.540261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2004009012
5-th percentile2163023113
Q13004006266
median5082013518
Q37138026760
95-th percentile8519005663
Maximum8765016012
Range6761007000
Interquartile range (IQR)4134020494

Descriptive statistics

Standard deviation2112498553
Coefficient of variation (CV)0.4197965736
Kurtosis-1.242789569
Mean5032195798
Median Absolute Deviation (MAD)2066504012
Skewness0.2188693089
Sum3.20953448e+13
Variance4.462650138e+18
MonotocityNot monotonic
2020-10-19T15:39:59.765803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21080330252< 0.1%
 
28400060072< 0.1%
 
42730040132< 0.1%
 
40150240282< 0.1%
 
83380090102< 0.1%
 
28360380832< 0.1%
 
52070150162< 0.1%
 
24470030162< 0.1%
 
53560070222< 0.1%
 
56570120182< 0.1%
 
53670090171< 0.1%
 
63410140081< 0.1%
 
72420340131< 0.1%
 
43240382111< 0.1%
 
63090150151< 0.1%
 
57180020071< 0.1%
 
30740290361< 0.1%
 
72810060641< 0.1%
 
42450140021< 0.1%
 
60230200211< 0.1%
 
58520120491< 0.1%
 
31400340411< 0.1%
 
21230170141< 0.1%
 
56240020441< 0.1%
 
87130250201< 0.1%
 
Other values (6343)634399.5%
 
ValueCountFrequency (%) 
20040090121< 0.1%
 
20040110111< 0.1%
 
20040150181< 0.1%
 
20050150281< 0.1%
 
20050170221< 0.1%
 
20060080371< 0.1%
 
20070080521< 0.1%
 
20070120431< 0.1%
 
20120210591< 0.1%
 
20120270151< 0.1%
 
ValueCountFrequency (%) 
87650160121< 0.1%
 
87650130241< 0.1%
 
87650060131< 0.1%
 
87640190031< 0.1%
 
87640050261< 0.1%
 
87640011091< 0.1%
 
87630220131< 0.1%
 
87620310441< 0.1%
 
87620200021< 0.1%
 
87620140211< 0.1%
 

realtyID
Real number (ℝ≥0)

Distinct6377
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1111176693
Minimum1110722482
Maximum1111598375
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:00.020116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1110722482
5-th percentile1110784566
Q11110958043
median1111202901
Q31111392666
95-th percentile1111554727
Maximum1111598375
Range875893
Interquartile range (IQR)434623

Descriptive statistics

Standard deviation246438.8378
Coefficient of variation (CV)0.0002217818637
Kurtosis-1.29134417
Mean1111176693
Median Absolute Deviation (MAD)206309
Skewness-0.1140609897
Sum7.085973769e+12
Variance6.073210075e+10
MonotocityNot monotonic
2020-10-19T15:40:00.242851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11107312871< 0.1%
 
11111995221< 0.1%
 
11109744821< 0.1%
 
11113306981< 0.1%
 
11111803581< 0.1%
 
11112838531< 0.1%
 
11114434691< 0.1%
 
11113952311< 0.1%
 
11108768851< 0.1%
 
11108700761< 0.1%
 
11107827921< 0.1%
 
11113897701< 0.1%
 
11113126571< 0.1%
 
11111865711< 0.1%
 
11113517381< 0.1%
 
11107764441< 0.1%
 
11112863751< 0.1%
 
11107873751< 0.1%
 
11114167551< 0.1%
 
11112935521< 0.1%
 
11108328101< 0.1%
 
11114431271< 0.1%
 
11111959901< 0.1%
 
11109704461< 0.1%
 
11113264261< 0.1%
 
Other values (6352)635299.6%
 
ValueCountFrequency (%) 
11107224821< 0.1%
 
11107233531< 0.1%
 
11107233691< 0.1%
 
11107239731< 0.1%
 
11107245871< 0.1%
 
11107251541< 0.1%
 
11107253511< 0.1%
 
11107253711< 0.1%
 
11107264961< 0.1%
 
11107266811< 0.1%
 
ValueCountFrequency (%) 
11115983751< 0.1%
 
11115748381< 0.1%
 
11115748201< 0.1%
 
11115748111< 0.1%
 
11115747411< 0.1%
 
11115745931< 0.1%
 
11115745091< 0.1%
 
11115744071< 0.1%
 
11115743591< 0.1%
 
11115743341< 0.1%
 

county
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
Los Angeles
6378 
ValueCountFrequency (%) 
Los Angeles6378100.0%
 
2020-10-19T15:40:00.446661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-19T15:40:00.592554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:40:00.699807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
s1275618.2%
 
e1275618.2%
 
L63789.1%
 
o63789.1%
 
63789.1%
 
A63789.1%
 
n63789.1%
 
g63789.1%
 
l63789.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5102472.7%
 
Uppercase Letter1275618.2%
 
Space Separator63789.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L637850.0%
 
A637850.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
s1275625.0%
 
e1275625.0%
 
o637812.5%
 
n637812.5%
 
g637812.5%
 
l637812.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6378100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6378090.9%
 
Common63789.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
s1275620.0%
 
e1275620.0%
 
L637810.0%
 
o637810.0%
 
A637810.0%
 
n637810.0%
 
g637810.0%
 
l637810.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
6378100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70158100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
s1275618.2%
 
e1275618.2%
 
L63789.1%
 
o63789.1%
 
63789.1%
 
A63789.1%
 
n63789.1%
 
g63789.1%
 
l63789.1%
 

subdivision
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct4593
Distinct (%)78.0%
Missing493
Missing (%)7.7%
Memory size50.0 KiB
REDONDO VILLA TR
 
27
1
 
21
6170
 
18
2
 
15
13
 
13
Other values (4588)
5791 
ValueCountFrequency (%) 
REDONDO VILLA TR270.4%
 
1210.3%
 
6170180.3%
 
2150.2%
 
13130.2%
 
61725100.2%
 
REDONDO VILLA100.2%
 
46018-0490.1%
 
560990.1%
 
100090.1%
 
1379680.1%
 
4460080.1%
 
LONG BEACH80.1%
 
645070.1%
 
1621570.1%
 
670.1%
 
295570.1%
 
EL SEGUNDO70.1%
 
REDONDO BEACH70.1%
 
5402570.1%
 
BELMONT HEIGHTS60.1%
 
1110460.1%
 
1001960.1%
 
6148960.1%
 
948860.1%
 
Other values (4568)564188.4%
 
(Missing)4937.7%
 
2020-10-19T15:40:00.932624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3797 ?
Unique (%)64.5%
2020-10-19T15:40:01.181278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length5.964722484
Min length1

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
131568.3%
 
228707.5%
 
328087.4%
 
426937.1%
 
524106.3%
 
621265.6%
 
020785.5%
 
720135.3%
 
919025.0%
 
818704.9%
 
14003.7%
 
A12113.2%
 
E11293.0%
 
n9862.6%
 
R9812.6%
 
O7922.1%
 
S7652.0%
 
T7622.0%
 
N7361.9%
 
L7321.9%
 
I5491.4%
 
a4931.3%
 
D4591.2%
 
H4321.1%
 
C4101.1%
 
Other values (18)22806.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2392662.9%
 
Uppercase Letter1101128.9%
 
Lowercase Letter14793.9%
 
Space Separator14003.7%
 
Dash Punctuation1570.4%
 
Other Punctuation700.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1315613.2%
 
2287012.0%
 
3280811.7%
 
4269311.3%
 
5241010.1%
 
621268.9%
 
020788.7%
 
720138.4%
 
919027.9%
 
818707.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n98666.7%
 
a49333.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A121111.0%
 
E112910.3%
 
R9818.9%
 
O7927.2%
 
S7656.9%
 
T7626.9%
 
N7366.7%
 
L7326.6%
 
I5495.0%
 
D4594.2%
 
H4323.9%
 
C4103.7%
 
M2742.5%
 
B2452.2%
 
G2442.2%
 
P2312.1%
 
U2212.0%
 
V2101.9%
 
W1911.7%
 
K1531.4%
 
Y1111.0%
 
F870.8%
 
J430.4%
 
X160.1%
 
Z150.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-157100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1400100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&5882.9%
 
/710.0%
 
#57.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2555367.2%
 
Latin1249032.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
1315612.4%
 
2287011.2%
 
3280811.0%
 
4269310.5%
 
524109.4%
 
621268.3%
 
020788.1%
 
720137.9%
 
919027.4%
 
818707.3%
 
14005.5%
 
-1570.6%
 
&580.2%
 
/7< 0.1%
 
#5< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A12119.7%
 
E11299.0%
 
n9867.9%
 
R9817.9%
 
O7926.3%
 
S7656.1%
 
T7626.1%
 
N7365.9%
 
L7325.9%
 
I5494.4%
 
a4933.9%
 
D4593.7%
 
H4323.5%
 
C4103.3%
 
M2742.2%
 
B2452.0%
 
G2442.0%
 
P2311.8%
 
U2211.8%
 
V2101.7%
 
W1911.5%
 
K1531.2%
 
Y1110.9%
 
F870.7%
 
J430.3%
 
Other values (3)430.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII38043100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
131568.3%
 
228707.5%
 
328087.4%
 
426937.1%
 
524106.3%
 
621265.6%
 
020785.5%
 
720135.3%
 
919025.0%
 
818704.9%
 
14003.7%
 
A12113.2%
 
E11293.0%
 
n9862.6%
 
R9812.6%
 
O7922.1%
 
S7652.0%
 
T7622.0%
 
N7361.9%
 
L7321.9%
 
I5491.4%
 
a4931.3%
 
D4591.2%
 
H4321.1%
 
C4101.1%
 
Other values (18)22806.0%
 

census
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.1%
Missing71
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.025210084
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:01.429421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.120901924
Coefficient of variation (CV)0.5534743938
Kurtosis1.305806505
Mean2.025210084
Median Absolute Deviation (MAD)1
Skewness1.144580893
Sum12773
Variance1.256421122
MonotocityNot monotonic
2020-10-19T15:40:01.583639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
1259040.6%
 
2190529.9%
 
3113917.9%
 
44827.6%
 
51352.1%
 
6420.7%
 
7110.2%
 
83< 0.1%
 
(Missing)711.1%
 
ValueCountFrequency (%) 
1259040.6%
 
2190529.9%
 
3113917.9%
 
44827.6%
 
51352.1%
 
6420.7%
 
7110.2%
 
83< 0.1%
 
ValueCountFrequency (%) 
83< 0.1%
 
7110.2%
 
6420.7%
 
51352.1%
 
44827.6%
 
3113917.9%
 
2190529.9%
 
1259040.6%
 

tract
Real number (ℝ≥0)

MISSING

Distinct1817
Distinct (%)28.8%
Missing71
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean445313.1741
Minimum0
Maximum980008
Zeros61
Zeros (%)1.0%
Memory size50.0 KiB
2020-10-19T15:40:01.796450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile113213
Q1215500.5
median408703
Q3601851
95-th percentile920015
Maximum980008
Range980008
Interquartile range (IQR)386350.5

Descriptive statistics

Standard deviation258664.7875
Coefficient of variation (CV)0.5808603979
Kurtosis-0.8793670321
Mean445313.1741
Median Absolute Deviation (MAD)193299
Skewness0.4109968413
Sum2808590189
Variance6.690747229e+10
MonotocityNot monotonic
2020-10-19T15:40:02.066820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0611.0%
 
901004330.5%
 
901205250.4%
 
800102240.4%
 
911001200.3%
 
577603180.3%
 
910210180.3%
 
920028170.3%
 
900805160.3%
 
920330160.3%
 
137103150.2%
 
143500150.2%
 
800329150.2%
 
901209150.2%
 
139600140.2%
 
139702140.2%
 
108202140.2%
 
910206130.2%
 
651304130.2%
 
275602130.2%
 
920015130.2%
 
920102130.2%
 
194300130.2%
 
900900130.2%
 
800101120.2%
 
Other values (1792)585491.8%
 
(Missing)711.1%
 
ValueCountFrequency (%) 
0611.0%
 
10111050.1%
 
1011222< 0.1%
 
10121040.1%
 
1012201< 0.1%
 
10130070.1%
 
10140080.1%
 
1021033< 0.1%
 
10210450.1%
 
1021073< 0.1%
 
ValueCountFrequency (%) 
98000850.1%
 
9303012< 0.1%
 
9301011< 0.1%
 
920339100.2%
 
920338110.2%
 
9203371< 0.1%
 
9203361< 0.1%
 
92033440.1%
 
9203321< 0.1%
 
92033140.1%
 

lot
Categorical

HIGH CARDINALITY
MISSING

Distinct483
Distinct (%)8.1%
Missing408
Missing (%)6.4%
Memory size50.0 KiB
1
836 
2
 
241
3
 
176
4
 
134
6
 
130
Other values (478)
4453 
ValueCountFrequency (%) 
183613.1%
 
22413.8%
 
31762.8%
 
41342.1%
 
61302.0%
 
71231.9%
 
51221.9%
 
111171.8%
 
91051.6%
 
81011.6%
 
13961.5%
 
12871.4%
 
14871.4%
 
15841.3%
 
19841.3%
 
18821.3%
 
16771.2%
 
10771.2%
 
24761.2%
 
21741.2%
 
17731.1%
 
25661.0%
 
20641.0%
 
22560.9%
 
23550.9%
 
Other values (458)274743.1%
 
(Missing)4086.4%
 
2020-10-19T15:40:02.333762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique183 ?
Unique (%)3.1%
2020-10-19T15:40:02.550166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length1.907024146
Min length1

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1284523.4%
 
2155312.8%
 
3121710.0%
 
410038.2%
 
58507.0%
 
n8166.7%
 
67796.4%
 
77165.9%
 
86895.7%
 
96605.4%
 
05784.8%
 
a4083.4%
 
A170.1%
 
C100.1%
 
B100.1%
 
I2< 0.1%
 
E2< 0.1%
 
N2< 0.1%
 
Q1< 0.1%
 
D1< 0.1%
 
P1< 0.1%
 
R1< 0.1%
 
F1< 0.1%
 
M1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1089089.5%
 
Lowercase Letter122410.1%
 
Uppercase Letter490.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1284526.1%
 
2155314.3%
 
3121711.2%
 
410039.2%
 
58507.8%
 
67797.2%
 
77166.6%
 
86896.3%
 
96606.1%
 
05785.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n81666.7%
 
a40833.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1734.7%
 
C1020.4%
 
B1020.4%
 
I24.1%
 
E24.1%
 
N24.1%
 
Q12.0%
 
D12.0%
 
P12.0%
 
R12.0%
 
F12.0%
 
M12.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1089089.5%
 
Latin127310.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
1284526.1%
 
2155314.3%
 
3121711.2%
 
410039.2%
 
58507.8%
 
67797.2%
 
77166.6%
 
86896.3%
 
96606.1%
 
05785.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n81664.1%
 
a40832.1%
 
A171.3%
 
C100.8%
 
B100.8%
 
I20.2%
 
E20.2%
 
N20.2%
 
Q10.1%
 
D10.1%
 
P10.1%
 
R10.1%
 
F10.1%
 
M10.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12163100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1284523.4%
 
2155312.8%
 
3121710.0%
 
410038.2%
 
58507.0%
 
n8166.7%
 
67796.4%
 
77165.9%
 
86895.7%
 
96605.4%
 
05784.8%
 
a4083.4%
 
A170.1%
 
C100.1%
 
B100.1%
 
I2< 0.1%
 
E2< 0.1%
 
N2< 0.1%
 
Q1< 0.1%
 
D1< 0.1%
 
P1< 0.1%
 
R1< 0.1%
 
F1< 0.1%
 
M1< 0.1%
 

zoning
Categorical

HIGH CARDINALITY

Distinct1068
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
LAR1
861 
LAR3
 
324
LARD1.5
 
157
LARS
 
151
LBR1N
 
147
Other values (1063)
4738 
ValueCountFrequency (%) 
LAR186113.5%
 
LAR33245.1%
 
LARD1.51572.5%
 
LARS1512.4%
 
LBR1N1472.3%
 
SCUR21201.9%
 
LAR21171.8%
 
LARD21061.7%
 
LARE11891.4%
 
LARA831.3%
 
LAC2751.2%
 
LCA11*711.1%
 
LARE15681.1%
 
LCA22*590.9%
 
TORR-LO590.9%
 
LCR1YY520.8%
 
LCA21*510.8%
 
LCA25*450.7%
 
SCUR3400.6%
 
LKR1YY390.6%
 
LAR4370.6%
 
GLR1YY360.6%
 
PSR6320.5%
 
BUR1YY310.5%
 
MNRS300.5%
 
Other values (1043)349854.8%
 
2020-10-19T15:40:02.767263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique514 ?
Unique (%)8.1%
2020-10-19T15:40:02.982746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length5
Mean length5.631232361
Min length3

Overview of Unicode Properties

Unique unicode characters56
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R579916.1%
 
L437312.2%
 
135509.9%
 
A33679.4%
 
021115.9%
 
*20975.8%
 
C19125.3%
 
213293.7%
 
Y10362.9%
 
S10102.8%
 
D9442.6%
 
P8672.4%
 
37992.2%
 
B6371.8%
 
56291.8%
 
M5581.6%
 
O5201.4%
 
U4121.1%
 
E3851.1%
 
N3771.0%
 
43701.0%
 
73190.9%
 
-3120.9%
 
G2490.7%
 
H2420.7%
 
Other values (31)17124.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2347565.4%
 
Decimal Number946926.4%
 
Other Punctuation23906.7%
 
Dash Punctuation3120.9%
 
Lowercase Letter2080.6%
 
Open Punctuation260.1%
 
Close Punctuation260.1%
 
Connector Punctuation10< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R579924.7%
 
L437318.6%
 
A336714.3%
 
C19128.1%
 
Y10364.4%
 
S10104.3%
 
D9444.0%
 
P8673.7%
 
B6372.7%
 
M5582.4%
 
O5202.2%
 
U4121.8%
 
E3851.6%
 
N3771.6%
 
G2491.1%
 
H2421.0%
 
W1950.8%
 
T1710.7%
 
F1330.6%
 
V960.4%
 
I860.4%
 
K700.3%
 
Z330.1%
 
X3< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1355037.5%
 
0211122.3%
 
2132914.0%
 
37998.4%
 
56296.6%
 
43703.9%
 
73193.4%
 
62282.4%
 
9690.7%
 
8650.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
*209787.7%
 
.1877.8%
 
:391.6%
 
/381.6%
 
&261.1%
 
,30.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-312100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(26100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)26100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t4722.6%
 
o3315.9%
 
r3215.4%
 
a2311.1%
 
e199.1%
 
y188.7%
 
c146.7%
 
l94.3%
 
p94.3%
 
s21.0%
 
n10.5%
 
u10.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2368365.9%
 
Common1223334.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R579924.5%
 
L437318.5%
 
A336714.2%
 
C19128.1%
 
Y10364.4%
 
S10104.3%
 
D9444.0%
 
P8673.7%
 
B6372.7%
 
M5582.4%
 
O5202.2%
 
U4121.7%
 
E3851.6%
 
N3771.6%
 
G2491.1%
 
H2421.0%
 
W1950.8%
 
T1710.7%
 
F1330.6%
 
V960.4%
 
I860.4%
 
K700.3%
 
t470.2%
 
o330.1%
 
Z330.1%
 
Other values (11)1310.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
1355029.0%
 
0211117.3%
 
*209717.1%
 
2132910.9%
 
37996.5%
 
56295.1%
 
43703.0%
 
73192.6%
 
-3122.6%
 
62281.9%
 
.1871.5%
 
9690.6%
 
8650.5%
 
:390.3%
 
/380.3%
 
(260.2%
 
)260.2%
 
&260.2%
 
_100.1%
 
,3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII35916100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R579916.1%
 
L437312.2%
 
135509.9%
 
A33679.4%
 
021115.9%
 
*20975.8%
 
C19125.3%
 
213293.7%
 
Y10362.9%
 
S10102.8%
 
D9442.6%
 
P8672.4%
 
37992.2%
 
B6371.8%
 
56291.8%
 
M5581.6%
 
O5201.4%
 
U4121.1%
 
E3851.1%
 
N3771.0%
 
43701.0%
 
73190.9%
 
-3120.9%
 
G2490.7%
 
H2420.7%
 
Other values (31)17124.8%
 

date
Categorical

HIGH CARDINALITY

Distinct575
Distinct (%)9.0%
Missing24
Missing (%)0.4%
Memory size50.0 KiB
2020-09-30
539 
2020-10-01
538 
2020-10-02
458 
2020-09-25
422 
2020-09-17
 
396
Other values (570)
4001 
ValueCountFrequency (%) 
2020-09-305398.5%
 
2020-10-015388.4%
 
2020-10-024587.2%
 
2020-09-254226.6%
 
2020-09-173966.2%
 
2020-09-183896.1%
 
2020-10-063775.9%
 
2020-09-243675.8%
 
2020-10-073175.0%
 
2020-10-083094.8%
 
2020-10-052974.7%
 
2020-10-092774.3%
 
2020-09-212584.0%
 
2020-09-282143.4%
 
2020-09-231682.6%
 
2020-09-291632.6%
 
2020-09-221502.4%
 
2020-09-161312.1%
 
2000-04-283< 0.1%
 
2013-08-153< 0.1%
 
2018-03-093< 0.1%
 
2017-08-312< 0.1%
 
2020-07-022< 0.1%
 
1992-11-102< 0.1%
 
1975-10-092< 0.1%
 
Other values (550)5678.9%
 
(Missing)240.4%
 
2020-10-19T15:40:03.212094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique533 ?
Unique (%)8.4%
2020-10-19T15:40:03.419155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.973659454
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
02162234.0%
 
21461823.0%
 
-1270820.0%
 
152288.2%
 
942106.6%
 
811831.9%
 
79421.5%
 
39051.4%
 
58831.4%
 
67121.1%
 
45290.8%
 
n480.1%
 
a24< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5083279.9%
 
Dash Punctuation1270820.0%
 
Lowercase Letter720.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
02162242.5%
 
21461828.8%
 
1522810.3%
 
942108.3%
 
811832.3%
 
79421.9%
 
39051.8%
 
58831.7%
 
67121.4%
 
45291.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-12708100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n4866.7%
 
a2433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6354099.9%
 
Latin720.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
02162234.0%
 
21461823.0%
 
-1270820.0%
 
152288.2%
 
942106.6%
 
811831.9%
 
79421.5%
 
39051.4%
 
58831.4%
 
67121.1%
 
45290.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n4866.7%
 
a2433.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII63612100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
02162234.0%
 
21461823.0%
 
-1270820.0%
 
152288.2%
 
942106.6%
 
811831.9%
 
79421.5%
 
39051.4%
 
58831.4%
 
67121.1%
 
45290.8%
 
n480.1%
 
a24< 0.1%
 

sale_price
Real number (ℝ≥0)

MISSING

Distinct1708
Distinct (%)27.7%
Missing220
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean1106153.902
Minimum500
Maximum75454545
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:03.622652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile190850
Q1499000
median705500
Q31114375
95-th percentile3005750
Maximum75454545
Range75454045
Interquartile range (IQR)615375

Descriptive statistics

Standard deviation1971336.301
Coefficient of variation (CV)1.782153729
Kurtosis427.3235604
Mean1106153.902
Median Absolute Deviation (MAD)265500
Skewness15.73524879
Sum6811695730
Variance3.886166813e+12
MonotocityNot monotonic
2020-10-19T15:40:03.839612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
650000641.0%
 
600000510.8%
 
700000480.8%
 
500000470.7%
 
550000460.7%
 
750000450.7%
 
800000450.7%
 
450000370.6%
 
850000340.5%
 
950000330.5%
 
560000330.5%
 
680000330.5%
 
1500000330.5%
 
630000330.5%
 
900000320.5%
 
610000320.5%
 
485000300.5%
 
1100000300.5%
 
1000000290.5%
 
615000290.5%
 
400000290.5%
 
585000290.5%
 
510000290.5%
 
1050000290.5%
 
350000280.4%
 
Other values (1683)525082.3%
 
(Missing)2203.4%
 
ValueCountFrequency (%) 
5002< 0.1%
 
10001< 0.1%
 
15001< 0.1%
 
30002< 0.1%
 
35002< 0.1%
 
40002< 0.1%
 
45001< 0.1%
 
500050.1%
 
55001< 0.1%
 
65002< 0.1%
 
ValueCountFrequency (%) 
754545451< 0.1%
 
405000001< 0.1%
 
376250001< 0.1%
 
367500001< 0.1%
 
300000003< 0.1%
 
265000001< 0.1%
 
210630001< 0.1%
 
200000001< 0.1%
 
190000001< 0.1%
 
186000001< 0.1%
 

estimated_value
Real number (ℝ≥0)

MISSING

Distinct3324
Distinct (%)57.2%
Missing562
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean883116.7469
Minimum104000
Maximum2998000
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:04.072325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum104000
5-th percentile349425
Q1544975
median712000
Q31050000
95-th percentile2108750
Maximum2998000
Range2894000
Interquartile range (IQR)505025

Descriptive statistics

Standard deviation522479.946
Coefficient of variation (CV)0.5916317948
Kurtosis2.217806564
Mean883116.7469
Median Absolute Deviation (MAD)214000
Skewness1.58959623
Sum5136207000
Variance2.72985294e+11
MonotocityNot monotonic
2020-10-19T15:40:04.286660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
632000100.2%
 
702000100.2%
 
568000100.2%
 
68400090.1%
 
60000090.1%
 
62500090.1%
 
61100090.1%
 
57500090.1%
 
58000090.1%
 
81300090.1%
 
54700080.1%
 
70500080.1%
 
62900080.1%
 
63100080.1%
 
67800080.1%
 
69300080.1%
 
77900080.1%
 
66300080.1%
 
60600080.1%
 
59600080.1%
 
57400080.1%
 
60800080.1%
 
59900080.1%
 
55000080.1%
 
106300080.1%
 
Other values (3299)560387.8%
 
(Missing)5628.8%
 
ValueCountFrequency (%) 
1040001< 0.1%
 
1250001< 0.1%
 
1350001< 0.1%
 
1510001< 0.1%
 
1560001< 0.1%
 
1620001< 0.1%
 
1740001< 0.1%
 
1800001< 0.1%
 
1810001< 0.1%
 
1820001< 0.1%
 
ValueCountFrequency (%) 
29980001< 0.1%
 
29340001< 0.1%
 
29100001< 0.1%
 
28940001< 0.1%
 
28930001< 0.1%
 
28820001< 0.1%
 
28730003< 0.1%
 
28630001< 0.1%
 
28410001< 0.1%
 
28330001< 0.1%
 

sex_offenders
Real number (ℝ≥0)

ZEROS

Distinct105
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.538726874
Minimum0
Maximum135
Zeros1078
Zeros (%)16.9%
Memory size50.0 KiB
2020-10-19T15:40:04.509943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile25
Maximum135
Range135
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.14694776
Coefficient of variation (CV)1.743921484
Kurtosis28.49620399
Mean7.538726874
Median Absolute Deviation (MAD)3
Skewness4.684316606
Sum48082
Variance172.8422353
MonotocityNot monotonic
2020-10-19T15:40:04.741411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0107816.9%
 
180512.6%
 
264210.1%
 
363710.0%
 
44106.4%
 
53705.8%
 
63325.2%
 
72333.7%
 
92003.1%
 
81963.1%
 
101792.8%
 
111692.6%
 
121482.3%
 
151061.7%
 
131051.6%
 
14841.3%
 
17621.0%
 
18590.9%
 
16540.8%
 
19440.7%
 
20310.5%
 
21290.5%
 
22270.4%
 
24250.4%
 
23250.4%
 
Other values (80)3285.1%
 
ValueCountFrequency (%) 
0107816.9%
 
180512.6%
 
264210.1%
 
363710.0%
 
44106.4%
 
53705.8%
 
63325.2%
 
72333.7%
 
81963.1%
 
92003.1%
 
ValueCountFrequency (%) 
1351< 0.1%
 
13340.1%
 
1321< 0.1%
 
1293< 0.1%
 
1281< 0.1%
 
1221< 0.1%
 
1151< 0.1%
 
1121< 0.1%
 
1043< 0.1%
 
992< 0.1%
 

crime_index
Categorical

MISSING

Distinct7
Distinct (%)0.1%
Missing900
Missing (%)14.1%
Memory size50.0 KiB
Slightly High
1646 
Low
1608 
Moderate
1600 
Very Low
427 
Moderately High
 
132
Other values (2)
 
65
ValueCountFrequency (%) 
Slightly High164625.8%
 
Low160825.2%
 
Moderate160025.1%
 
Very Low4276.7%
 
Moderately High1322.1%
 
High570.9%
 
Very High80.1%
 
(Missing)90014.1%
 
2020-10-19T15:40:04.957413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-19T15:40:05.086364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:40:05.274280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length8
Mean length7.434619003
Min length3

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e38998.2%
 
o37677.9%
 
i34897.4%
 
g34897.4%
 
h34897.4%
 
l34247.2%
 
t33787.1%
 
a26325.6%
 
y22134.7%
 
22134.7%
 
r21674.6%
 
L20354.3%
 
w20354.3%
 
H18433.9%
 
n18003.8%
 
M17323.7%
 
d17323.7%
 
S16463.5%
 
V4350.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3751479.1%
 
Uppercase Letter769116.2%
 
Space Separator22134.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L203526.5%
 
H184324.0%
 
M173222.5%
 
S164621.4%
 
V4355.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e389910.4%
 
o376710.0%
 
i34899.3%
 
g34899.3%
 
h34899.3%
 
l34249.1%
 
t33789.0%
 
a26327.0%
 
y22135.9%
 
r21675.8%
 
w20355.4%
 
n18004.8%
 
d17324.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2213100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4520595.3%
 
Common22134.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e38998.6%
 
o37678.3%
 
i34897.7%
 
g34897.7%
 
h34897.7%
 
l34247.6%
 
t33787.5%
 
a26325.8%
 
y22134.9%
 
r21674.8%
 
L20354.5%
 
w20354.5%
 
H18434.1%
 
n18004.0%
 
M17323.8%
 
d17323.8%
 
S16463.6%
 
V4351.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2213100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII47418100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e38998.2%
 
o37677.9%
 
i34897.4%
 
g34897.4%
 
h34897.4%
 
l34247.2%
 
t33787.1%
 
a26325.6%
 
y22134.7%
 
22134.7%
 
r21674.6%
 
L20354.3%
 
w20354.3%
 
H18433.9%
 
n18003.8%
 
M17323.7%
 
d17323.7%
 
S16463.5%
 
V4350.9%
 

enviornmental_hazards
Real number (ℝ≥0)

Distinct60
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.977265601
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:05.465695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile19
Maximum91
Range90
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.896166475
Coefficient of variation (CV)0.9883766606
Kurtosis22.36010306
Mean6.977265601
Median Absolute Deviation (MAD)2
Skewness3.684877597
Sum44501
Variance47.55711205
MonotocityNot monotonic
2020-10-19T15:40:05.677174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3105516.5%
 
2102116.0%
 
484213.2%
 
55578.7%
 
64737.4%
 
73996.3%
 
83665.7%
 
92684.2%
 
102123.3%
 
111882.9%
 
121472.3%
 
131161.8%
 
14961.5%
 
1931.5%
 
15641.0%
 
16580.9%
 
17470.7%
 
19360.6%
 
21350.5%
 
18330.5%
 
22290.5%
 
20230.4%
 
23190.3%
 
32140.2%
 
24140.2%
 
Other values (35)1732.7%
 
ValueCountFrequency (%) 
1931.5%
 
2102116.0%
 
3105516.5%
 
484213.2%
 
55578.7%
 
64737.4%
 
73996.3%
 
83665.7%
 
92684.2%
 
102123.3%
 
ValueCountFrequency (%) 
911< 0.1%
 
841< 0.1%
 
831< 0.1%
 
801< 0.1%
 
721< 0.1%
 
701< 0.1%
 
671< 0.1%
 
662< 0.1%
 
651< 0.1%
 
551< 0.1%
 
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
1
4982 
2
1318 
0
 
62
3
 
16
ValueCountFrequency (%) 
1498278.1%
 
2131820.7%
 
0621.0%
 
3160.3%
 
2020-10-19T15:40:05.885472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-19T15:40:06.008962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:40:06.146608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1498278.1%
 
2131820.7%
 
0621.0%
 
3160.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number6378100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1498278.1%
 
2131820.7%
 
0621.0%
 
3160.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6378100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1498278.1%
 
2131820.7%
 
0621.0%
 
3160.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6378100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1498278.1%
 
2131820.7%
 
0621.0%
 
3160.3%
 

school quality
Categorical

MISSING

Distinct4
Distinct (%)0.1%
Missing92
Missing (%)1.4%
Memory size50.0 KiB
Average
2207 
Excellent
1695 
Above Average
1665 
Poor
719 
ValueCountFrequency (%) 
Average220734.6%
 
Excellent169526.6%
 
Above Average166526.1%
 
Poor71911.3%
 
(Missing)921.4%
 
2020-10-19T15:40:06.326256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-19T15:40:06.461744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:40:07.288808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length8.701944183
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1279923.1%
 
A553710.0%
 
v553710.0%
 
r45918.3%
 
a39647.1%
 
g38727.0%
 
l33906.1%
 
o31035.6%
 
n18793.4%
 
E16953.1%
 
x16953.1%
 
c16953.1%
 
t16953.1%
 
b16653.0%
 
16653.0%
 
P7191.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4588582.7%
 
Uppercase Letter795114.3%
 
Space Separator16653.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A553769.6%
 
E169521.3%
 
P7199.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1279927.9%
 
v553712.1%
 
r459110.0%
 
a39648.6%
 
g38728.4%
 
l33907.4%
 
o31036.8%
 
n18794.1%
 
x16953.7%
 
c16953.7%
 
t16953.7%
 
b16653.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1665100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5383697.0%
 
Common16653.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1279923.8%
 
A553710.3%
 
v553710.3%
 
r45918.5%
 
a39647.4%
 
g38727.2%
 
l33906.3%
 
o31035.8%
 
n18793.5%
 
E16953.1%
 
x16953.1%
 
c16953.1%
 
t16953.1%
 
b16653.1%
 
P7191.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
1665100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55501100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1279923.1%
 
A553710.0%
 
v553710.0%
 
r45918.3%
 
a39647.1%
 
g38727.0%
 
l33906.1%
 
o31035.6%
 
n18793.4%
 
E16953.1%
 
x16953.1%
 
c16953.1%
 
t16953.1%
 
b16653.0%
 
16653.0%
 
P7191.3%
 

url
URL

Distinct6370
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
https://www.realtytrac.com/property/ca/santa-monica/90405/2207-21st-st/52072528/
 
2
https://www.realtytrac.com/property/ca/la-canada-flintridge/91011/4033-chevy-chase-dr/52148918/
 
2
https://www.realtytrac.com/property/ca/pomona/91767/735-verde-vista-ave/154960595/
 
2
https://www.realtytrac.com/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/
 
2
https://www.realtytrac.com/property/ca/burbank/91506/631-n-lincoln-st/154425557/
 
2
Other values (6365)
6368 
ValueCountFrequency (%) 
https://www.realtytrac.com/property/ca/santa-monica/90405/2207-21st-st/52072528/2< 0.1%
 
https://www.realtytrac.com/property/ca/la-canada-flintridge/91011/4033-chevy-chase-dr/52148918/2< 0.1%
 
https://www.realtytrac.com/property/ca/pomona/91767/735-verde-vista-ave/154960595/2< 0.1%
 
https://www.realtytrac.com/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/2< 0.1%
 
https://www.realtytrac.com/property/ca/burbank/91506/631-n-lincoln-st/154425557/2< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90031/4025-berenice-pl/154664929/2< 0.1%
 
https://www.realtytrac.com/property/ca/canoga-park/91306/7602-irondale-ave/151724894/2< 0.1%
 
https://www.realtytrac.com/property/ca/canyon-country/91351/19845-collins-rd-347/14163321/2< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90062/4314-dalton-ave/143681327/1< 0.1%
 
https://www.realtytrac.com/property/ca/santa-monica/90405/2411-3rd-st-d/145906302/1< 0.1%
 
https://www.realtytrac.com/property/ca/long-beach/90805/5500-ackerfield-ave-205/21757976/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90011/350-e-jefferson-blvd/154650076/1< 0.1%
 
https://www.realtytrac.com/property/ca/el-monte/91733/2621-humbert-ave/154937011/1< 0.1%
 
https://www.realtytrac.com/property/ca/playa-del-rey/90293/7548-trask-ave/43737843/1< 0.1%
 
https://www.realtytrac.com/property/ca/lancaster/93534/45655-17th-st-w/20513418/1< 0.1%
 
https://www.realtytrac.com/property/ca/monrovia/91016/370-grand-ave/154976025/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90034/3439-keystone-ave-1/13166437/1< 0.1%
 
https://www.realtytrac.com/property/ca/north-hollywood/91601/5915-riverton-ave/17291572/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90042/4939-sycamore-ter/26820987/1< 0.1%
 
https://www.realtytrac.com/property/ca/sun-valley/91352/8700-rincon-ave/52819998/1< 0.1%
 
https://www.realtytrac.com/property/ca/hacienda-heights/91745/2428-fidelidad-dr/154946539/1< 0.1%
 
https://www.realtytrac.com/property/ca/el-segundo/90245/512-e-imperial-ave/330491512/1< 0.1%
 
https://www.realtytrac.com/property/ca/whittier/90602/12908-lambert-rd/16861635/1< 0.1%
 
https://www.realtytrac.com/property/ca/northridge/91324/8436-wilbur-ave/52777230/1< 0.1%
 
https://www.realtytrac.com/property/ca/studio-city/91604/11847-laurelwood-dr-207/30603985/1< 0.1%
 
Other values (6345)634599.5%
 
ValueCountFrequency (%) 
https6378100.0%
 
ValueCountFrequency (%) 
www.realtytrac.com6378100.0%
 
ValueCountFrequency (%) 
/property/ca/los-angeles/90031/4025-berenice-pl/154664929/2< 0.1%
 
/property/ca/santa-monica/90405/2207-21st-st/52072528/2< 0.1%
 
/property/ca/burbank/91506/631-n-lincoln-st/154425557/2< 0.1%
 
/property/ca/canyon-country/91351/19845-collins-rd-347/14163321/2< 0.1%
 
/property/ca/pomona/91767/735-verde-vista-ave/154960595/2< 0.1%
 
/property/ca/alhambra/91803/1428-s-marengo-ave/326696311/2< 0.1%
 
/property/ca/canoga-park/91306/7602-irondale-ave/151724894/2< 0.1%
 
/property/ca/la-canada-flintridge/91011/4033-chevy-chase-dr/52148918/2< 0.1%
 
/property/ca/whittier/90602/13311-addington-st/17188692/1< 0.1%
 
/property/ca/altadena/91001/503-devonwood-rd/27280256/1< 0.1%
 
/property/ca/azusa/91702/5814-n-rockvale-ave/52343806/1< 0.1%
 
/property/ca/downey/90242/12114-eastbrook-ave/150492908/1< 0.1%
 
/property/ca/inglewood/90303/10932-atkinson-ave/154484278/1< 0.1%
 
/property/ca/carson/90746/17505-merimac-ct/154691550/1< 0.1%
 
/property/ca/whittier/90604/16258-silvergrove-dr/154931645/1< 0.1%
 
/property/ca/palmdale/93550/2230-e-avenue-q4-38/7234381/1< 0.1%
 
/property/ca/sherman-oaks/91403/3634-royal-woods-dr/141643297/1< 0.1%
 
/property/ca/san-dimas/91773/2084-paseo-ambar/145186847/1< 0.1%
 
/property/ca/west-hills/91304/8100-lena-ave/17518460/1< 0.1%
 
/property/ca/roosevelt/93532/vac70th-st-e-pav-vic-avenue/241291307/1< 0.1%
 
/property/ca/northridge/91324/9213-wystone-ave/10194041/1< 0.1%
 
/property/ca/lakewood/90715/11610-walcroft-st/19367791/1< 0.1%
 
/property/ca/los-angeles/90068/2156-hollyridge-dr/52040057/1< 0.1%
 
/property/ca/monterey-park/91755/821-hershey-ave/154682229/1< 0.1%
 
/property/ca/hawthorne/90250/5401-w-149th-pl-5/155018464/1< 0.1%
 
Other values (6345)634599.5%
 
ValueCountFrequency (%) 
6378100.0%
 
ValueCountFrequency (%) 
6378100.0%
 

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct35
Distinct (%)0.6%
Missing543
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean3.43924593
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:07.525064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum96
Range95
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.268134257
Coefficient of variation (CV)0.9502473286
Kurtosis272.2895293
Mean3.43924593
Median Absolute Deviation (MAD)1
Skewness13.76666756
Sum20068
Variance10.68070152
MonotocityNot monotonic
2020-10-19T15:40:07.736821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
3231736.3%
 
2134021.0%
 
4127219.9%
 
53806.0%
 
12544.0%
 
6971.5%
 
7330.5%
 
8320.5%
 
9180.3%
 
10160.3%
 
12120.2%
 
1490.1%
 
1670.1%
 
1160.1%
 
2040.1%
 
2840.1%
 
153< 0.1%
 
243< 0.1%
 
303< 0.1%
 
263< 0.1%
 
663< 0.1%
 
132< 0.1%
 
182< 0.1%
 
362< 0.1%
 
172< 0.1%
 
Other values (10)110.2%
 
(Missing)5438.5%
 
ValueCountFrequency (%) 
12544.0%
 
2134021.0%
 
3231736.3%
 
4127219.9%
 
53806.0%
 
6971.5%
 
7330.5%
 
8320.5%
 
9180.3%
 
10160.3%
 
ValueCountFrequency (%) 
961< 0.1%
 
731< 0.1%
 
691< 0.1%
 
663< 0.1%
 
581< 0.1%
 
401< 0.1%
 
391< 0.1%
 
362< 0.1%
 
341< 0.1%
 
303< 0.1%
 

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)0.6%
Missing543
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.685347044
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size50.0 KiB
2020-10-19T15:40:07.941938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.091682616
Coefficient of variation (CV)1.151315851
Kurtosis305.6214816
Mean2.685347044
Median Absolute Deviation (MAD)1
Skewness14.34155149
Sum15669
Variance9.558501401
MonotocityNot monotonic
2020-10-19T15:40:08.145204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
2237737.3%
 
3165726.0%
 
1103716.3%
 
44086.4%
 
51382.2%
 
6851.3%
 
7260.4%
 
8250.4%
 
9150.2%
 
10110.2%
 
1450.1%
 
2050.1%
 
1540.1%
 
1640.1%
 
1140.1%
 
123< 0.1%
 
303< 0.1%
 
183< 0.1%
 
222< 0.1%
 
372< 0.1%
 
192< 0.1%
 
462< 0.1%
 
172< 0.1%
 
282< 0.1%
 
651< 0.1%
 
Other values (12)120.2%
 
(Missing)5438.5%
 
ValueCountFrequency (%) 
1103716.3%
 
2237737.3%
 
3165726.0%
 
44086.4%
 
51382.2%
 
6851.3%
 
7260.4%
 
8250.4%
 
9150.2%
 
10110.2%
 
ValueCountFrequency (%) 
991< 0.1%
 
701< 0.1%
 
661< 0.1%
 
651< 0.1%
 
471< 0.1%
 
462< 0.1%
 
451< 0.1%
 
411< 0.1%
 
391< 0.1%
 
372< 0.1%
 

Interactions

2020-10-19T15:38:59.108641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:38:59.398568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:38:59.643918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:38:59.945492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:00.190410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:00.421055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:00.650888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:00.876904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:01.078649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:01.265241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:01.475432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:02.094071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:02.297878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:02.492202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:02.687056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:02.880431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:03.090213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:03.298053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:03.507709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:03.713897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:03.924313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-19T15:39:50.267906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-19T15:40:08.353919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-19T15:40:08.761909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-19T15:40:09.133755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-19T15:40:09.517642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-19T15:40:09.869608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-19T15:39:50.945507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:52.572694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:53.241834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-19T15:39:53.935201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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334.07296-118.066909259 Ramona BlvdSingle Family Residence1682.000007000.000001978.0000085940270161111055994.00000Los AngelesROSEMEAD2.00000432901.000006RMPOD*2020-09-28738000.00000752000.000003Moderate111Excellenthttps://www.realtytrac.com/property/ca/rosemead/91770/9259-ramona-blvd/154986110/3.000002.00000
433.77772-118.15491825 Obispo AveTriplex (3 units, any combination)1958.000006754.000001938.0000072580130161111056010.00000Los Angeles12.00000576904.0000018LBR2N2020-09-281185000.00000989000.0000011Slightly High81Averagehttps://www.realtytrac.com/property/ca/long-beach/90804/825-obispo-ave/44027788/4.000003.00000
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Last rows

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6377-118.4534034.4002326589 Oakdale Canyon LnSingle Family Residence2738.000005272.000002000.0000028410310241111183290.00000Los Angeles47200-021.00000920043.0000026SCSP2020-09-29735000.00000766700.000000NaN32Excellenthttps://www.realtytrac.com/property/ca/canyon-cntry/91387/26589-oakdale-canyon-ln/23738306/5.000003.00000